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The update allows VGG to process images larger or smaller than prescribed imagenet size using adaptive average pooling. Will be useful while finetuning or testing on different resolution images. Similar to https://github.com/pytorch/vision/pull/643 and https://github.com/pytorch/vision/pull/672. I did not include adaptive avg pool in features or classifier block so that these predefined blocks can be used as it is.

The update allows Alexnet to process images larger or smaller than prescribed imagenet size using adaptive average pooling. Will be useful while finetuning or testing on different resolution images. Similar to https://github.com/pytorch/vision/pull/643 and https://github.com/pytorch/vision/pull/672. I did not include adaptive avg pool in features or classifier block so that these predefined blocks can be used as it is.

* Update URL for EMNIST

Update the dataset and download URL for EMNIST to the url from the updated arxiv version.

* Update mnist.py

* Added size information for inception v3

Since inception v3 expects in contrast to the other models a size
of 299x299x3 I added a small note in the docstring to point that out
(addresses #560)

* inception size info in note box; fixed line length

* removed trailing whitespace

* doc change

* doc changes

* lint_checks

* Update transforms.py

* normalise

* some changes

* Update functional.py

* Update functional.py

* code changes

* Add KMNIST

* Add documentation

* Add KMNIST to module's __all__ list

* Add Cityscapes Dataset

* Rename 'label' to 'semantic' to make the meaning more clear

* Add support for gtCoarse target set

* docs added

* Update transforms.py

* Update transforms.py

* modes added

* tests_added

* Update test_transforms.py

* Update test_transforms.py

* Update test_transforms.py

* bug fixes to_tensor

* tensor checked

* pad updated

* checked pad

* pad checked

* pad checked

* VOC Dataset, linted, flak8 passing, samples on gist.

* Double backtick on values.

* Apply suggestions from code review

Add suggestions from @ellisbrown, using dict of dicts instead of array index.
Co-Authored-By: bpinaya <bpg_92@hotmail.com>

* Fixed errors with the new comments.

* Added documentation on RST

* Removed getBB, added parse_voc_xml, variable naming change.

* Removed unused variable, removed VOC_CLASSES, two new gists for test.

* Zero-init the residual branch in resnet

* Add zero_init_residual as an option

* added separate checks for dimensionality in to_pil_image and added tests

* updated to_pil_image to use both 2D ndarrays and tensors, as well as refactored the tests

* Add Flickr8k and Flickr30k Datasets

* Add Flickr to the dataset docs

* Sort ids, glob during construction

* annFile -> ann_file

* Fix undefined variable name bug

* Add SBU Captioned Photo Dataset

* Add SBU to the dataset docs

* Changing to AdaptiveAvgPool2d

* add whitespace

* Update densenet.py

* Update densenet.py

* use predefined function

* cleanup resnet

* fix stride

* revert inplanes since it isnt constant

* lint

* AdaptiveAvgpool2d integrated

The Average Pool Layer on SqueezeNet and ResNet was hardcoded, this was changed, now any the input size is accepted.

* error fixed

* linting fixed

* Revert "move area calculation out of loop (#641)"

This reverts commit 62cbf0bf.

* Revert "Fix FashionMNIST loading MNIST (#640)"

This reverts commit 36215690.

* move area calculation out of loop

No need to calculate area 10 times when it doesn't change.

* lint

Before this would lead FashionMNIST to contain mnist data:

```
MNIST(root, download=True)
FashionMNIST(root, download=True)
```

As MNIST and FashionMNIST are the same classes, the processed
outputs actual ended up to be the same files. This commit now
stores them at different files and also stores the class name when
saving them. I also added md5 sums.

* Fix inception v3 input transform for trace & onnx

* Input transform are in-place updates, which produce issues for tracing
and exporting to onnx.

* nit

* cast mnist targer to int

* fix unused variables

* fix syntax in tests

* remove # noqa; rename l variable to line_split; use except OSError

* add W504

* add W504

* add W503,W504 tox.ini

Check if `lambd` is callable instead of checking if its a `types.Lambda` object.